Animation Cartography - Intrinsic Reconstruction of Shape and Motion

In this paper, we consider the problem of animation reconstruction, i.e., the
reconstruction of shape and motion of a deformable object from dynamic
3D scanner data, without using user provided template models. Unlike pre-
vious work that addressed this problem, we do not rely on locally conver-
gent optimization but present a system that can handle fast motion, tem-
porally disrupted input, and can correctly match objects that disappear for
extended time periods in acquisition holes due to occlusion. Our approach
is motivated by cartography: We first estimate a few landmark correspon-
dences, which are extended to a dense matching and then used to recon-
struct geometry and motion. We propose a number of algorithmic building
blocks: a scheme for tracking landmarks in temporally coherent and inco-
herent data, an algorithm for robust estimation of dense correspondences
under topological noise, and the integration of local matching techniques to
refine the result. We describe and evaluate the individual components and
propose a complete animation reconstruction pipeline based on these ideas.
We evaluate our method on a number of standard benchmark data sets and
show that we can obtain correct reconstructions in situations where other
techniques fail completely or require additional user guidance such as a
template model.

Projects

Time-resolved 3D Capture of Non-stationary Gas Flows

Abstract

Fluid simulation is one of the most active research areas in computer graphics. However, it remains difficult to obtain measurements of real fluid flows for validation of the simulated data.
In this paper, we take a step in the direction of capturing flow data for such purposes. Specifically, we present the first time-resolved Schlieren tomography system for capturing full 3D, non-stationary gas flows on a dense volumetric grid. Schlieren tomography uses 2D ray deflection measurements to reconstruct a time-varying grid of 3D refractive index values, which directly correspond to physical properties of the flow. We derive a new solution for this reconstruction problem that lends itself to efficient algorithms to robustly work with relatively small numbers of cameras. Our physical system is easy to set up, and consists of an array of relatively low cost rolling-shutter camcorders that are synchronized with a new approach. We demonstrate our method with real measurements, and analyze precision with synthetic data for which ground truth information is available.